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19 August 20081 Case Studies in Quality by Design with Design of Experiments From Pharmaceutical Technology Lynn Torbeck 19 August 2008.

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Presentation on theme: "19 August 20081 Case Studies in Quality by Design with Design of Experiments From Pharmaceutical Technology Lynn Torbeck 19 August 2008."— Presentation transcript:

1 19 August Case Studies in Quality by Design with Design of Experiments From Pharmaceutical Technology Lynn Torbeck 19 August 2008

2 2 Overview A little, very little, history 3 types of controlled experiments Key literature and dates Today’s driving force behind QbD “Show me an example in my area of interest” Case Studies from Pharm Tech

3 19 August 20083

4 4 A Short Bit of History Sir Ronald A. Fisher Born 1890, England Died 1962, Australia Graduated college in 1913, math, genetics 1919 joined Rothamsted Experimental Station in Harpenden, England The right person in the right place.

5 19 August Three Controlled Experiments John S. Mill, System of Logic, Success / Failure One run, no factors varied, one outcome, yes/no Easy to design, easy to analyze Lack of comparison, inefficient 2. OFAT, One-Factor-at-a-Time We all learned this in school Several runs, one factor varied, two outcomes Easy to Design, has comparison of outcomes Can’t find interactions and is inefficient

6 19 August Fisher’s Experiments Multiple runs, multiple factors varied Multiple outcomes Will find interactions Is much more efficient Comparison of outcomes

7 19 August Key Literature 1926, “The Arrangements of Field Experiments.” Journal of the Ministry of Agriculture of Great Britain. Fisher. 1935, The Design of Experiments, Oliver & Boyd, London. Fisher. 1951, “On the Experimental Attainment of Optimum Conditions,” Box and Wilson. The original source for QbD !

8 19 August Today’s Driving Force FDA / PAT guidance ICH Q8 – Quality by Design ICH Q8 _ Annex with DOE example The freedom of Design Space Ability to change within Design Space Economics and cost savings Product / Process Knowledge

9 19 August State of the Topic While there is more to Quality by Design than DOE, it seems to be the part that most people have the most trouble with. Chemometrics is many times more complicated than DOE but yet it seems to be more readily accepted than DOE.

10 19 August Show Me an Example Many people have taken a DOE class at some time, but still have difficulty in getting started. The most common request is for examples in specific areas. Examples here show that it is not all that difficult to get started. QbD was being done before ICH Q8

11 19 August Six Steps to Designing 1. Do your homework 2. Define the measured responses (CQA) 3. Brainstorm factors (CPP) 4. Select 2-7 factors to be treatments 5. Select levels or values for treatments 6. Select a design

12 19 August A Short List of Designs

13 19 August Pharm Tech Yearbook, 1999 “Functionality Testing of a Co-processed Diluent Containing Lactose and Microcrystalline Cellulose” Gohel, M., et all Pre-formulation development of excipients

14 19 August Objective “The objective of the present study was to prepare the directly compressible adjuvant by using a simpler process that could be adopted by any pharmaceutical company. Product is a tablet

15 19 August Treatments A: Ratio of lactose to MCC 75:25, 85:15 Binding Agent Dextrin, HPMC % binding agent 1.0%, 1.5%

16 19 August Held Constant Stirring speed at 35 rpm Stirring time at 90 minutes

17 19 August Agglomerate Responses Bulk Density, Tapped Density Angle of Repose, Flow Rate Hausner ratio Carr’s Index Friability Index Moisture uptake

18 19 August Statistical Design Three treatments Each at two levels Eight sets of conditions or runs A 2 3 full factorial design

19 19 August Results This is a complicated set of data with many two factor interactions, but it can be understood by looking at a geometric presentation of the factors and the responses for flow rate. Ratio is on the horizontal, A, axis Agent is on the vertical, B, axis Percent is on the third, C, axis

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21 19 August Observations for Flow Rate 1. Within these bounds, flow is 14.0 to 19.0 g/s 2. Slowest is 85/15, HPMC, 1.5%. 3. Fastest is 75/25, HPMC, 1.5% 4. Fast is 85/15, Dextrin, 1.0%

22 19 August Pharm Tech, November 1999 This is a related example. “An Investigation of the Direct- Compression Characteristics of Co- processed Lactose-Microcrystalline Cellulose Using Statistical Design.” Gohel, M., and Jogami, P.

23 19 August Pharm Tech, June, 1993 A bottle packaging example. “The Effect of Rayon Coiler on the Dissolution of Hard-Shell Gelatin Capsules. Hartauer, K.; Bucko, J.; Cooke, G; Mayer, R.; Schwier, J. and Sullivan, G.

24 19 August BioPharm, October 1997 “Demonstrating Process Robustness for Chromatographic Purification of a Recombinant Protein.” Kelly, B.; Jennings, P.; Wright, R. and Briasco, C.

25 19 August Objective “Control is achieved by setting operating ranges for manipulated process variables. Those ranges should ensure that a process does not fail within the multidimensional operating space defined by those limits.” That is, the Design Space !

26 19 August Treatments 1. Load Mass 2.4 – Load Conductivity 2.5 – % Cleavage 63 – Wash pH 9.4 – Wash volume 9.7 – Elution pH 9.4 – Elution conductivity 8.6 – 14.4

27 19 August Responses 1. Recovery % 2. Purity % 3. rhIL-11 mass 4. Product pool volume 5. Elution pool concentration

28 19 August Statistical Design Wash pH / Wash volume confounded Elution pH / Elution conductivity confounded 1. Five factors each at two levels runs will still find the two factor interactions 3. Design is a fractional factorial

29 19 August

30 19 August Design Space Independent Factor Space ? Dependent Response Space

31 19 August Conceptual Design Space Uncertain space Region of operability Operation Space Opt Region of Interest

32 19 August Statistical Design Space “The mathematically and statistically defined combination of Factor Space and Response Space that results in a system, product or process that consistently meets its quality characteristics, SSQuIP, with a high degree of assurance.” LDT

33 19 August Analysis Analysis is done by fitting a mathematical model to the factors (CPP) and the responses (CQA) that includes the factor main effects and the significant two factor interactions The model is then used to find contour plots for recovery and purity.

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37 19 August Pharm Tech, February 1999 “Blow-Fill-Seal Technology: Part II, Design Optimization of a Particulate Control System.” Price, J.

38 19 August Objectives 1. Optimize the particulate control system 2. Find cause and effect relationships 3. Alter the system settings to improve performance 4. Find interactions between factors

39 19 August Treatments 1. HEPA flow rate % Damper % open Chimney air ft/min HEPA height in Isolation plateSlotted – Hole 6. Knife cutDouble Single

40 19 August Response Particulate level. Three measurements at each of the 24 conditions

41 19 August Statistical Design Six factors Three at two levels Three at three levels 16 combinations 8 center points Design is a fractional factorial Design is resolution IV

42 19 August Analysis Analysis of Variance, ANOVA, was used. 15 effects were included 5 were statistically significant Damper HEPA height Knife cur Isolation plate HEPA flow * HEPA height OR {damper*knife cut}

43 19 August Conclusions “The study met the design objective of minimizing the particulate levels while the particulate control system operated in the dynamic state. … a more thorough understanding of the cause and effect relationships between the critical input factors and the particulate levels was obtained using the DOE.”

44 19 August Pharm Tech, Analytical Validation, 1999 Robustness Testing of an HPLC Method Using Experimental Design.” Peters, P. and Paino, T.

45 19 August Objective “This article describes an experimental design that challenged an analytical method that assays two components in a solid dosage drug product.” Confirm the robustness of an HPLC method.

46 19 August Treatments HPLC systemA, B HPLC columnY, X Wavelength A270, 290 B215, 235 Flow rate0.7, 1.3

47 19 August Treatments Injection volume10, 30 Column tempAmbient, 30 Mobile phase TFA85, 75 MeCN15, 25

48 19 August Responses 1. Resolution of component A and B 2. Theoretical plates for A and B 3. Tailing factor for A and B 4. %RSD of the peaks for A and B

49 19 August Statistical Design 7 factors each at two levels Wavelength A and B are confounded Mobile phase TFA and MeCN are confounded 8 runs done in triplicate for 24 total Design is a fractional factorial Design is resolution III.

50 19 August Analysis and Results Visual inspection of an overlay of the 8 chromatograms shows that the method is robust within the tolerance limits of the parameters tested. They have acceptable resolution and peak shape.

51 19 August Compare Chromatograms

52 19 August Pharm Tech, May 1998 “A Systematic Formulation Optimization Process for a Generic Pharmaceutical Tablet.” Hwang, R.; Gemoules, M; Ramlose, D. and Thomasson, C.

53 19 August Objective “ … optimizing an immediate release tablet formulation for a generic pharmaceutical product.” Develop a generic tablet with a disintegration time of 6-12 minutes, 5 minute dissolution of 40-60% and 45 minute dissolution of greater than 90%.

54 19 August Treatments API particle sizesmall large API %5% 10% Lactose MCC ratio 1:3 3:1 MCC particle sizesmall large MCC densitylow high

55 19 August Treatments Disintegrantcornstarch, glycolate Disintegrant %1% 5% Talc0 5% Mag Sterate0.5% 1%

56 19 August Responses Blend homogeneity Compression force %RSD Ejection force Tablet weight %RSD Tablet hardness

57 19 August Responses Tablet friability Tablet disintegration time Tablet dissolution at 5 minutes Tablet dissolution at 45 minutes

58 19 August Statistical Design 9 factors each at two levels 16 runs Design is a fractional factorial Resolution III

59 19 August The best formulation: API7.14% Fast-Flo lactose60.74% Avicel PH % Talc1% Mag Stearate0.75%

60 19 August Conclusion “The formulation was successfully scaled up to a 120 kg batch size and the manufacturability and product quality were confirmed.” “This study has demonstrated the efficiency and effectiveness of using a systematic formulation optimization process … “

61 19 August Pharm Tech, March 1994 “Evaluation of a Cartridge and a Bag Filer System in Fluid-Bed Drying. Bolyard, K. and McCurdy, V.

62 19 August Pharm Tech Europe, April 2000 “Response Surface Methodology Applied to Fluid Bed Granulation.” Wehrle, P. et all

63 19 August Pharm Tech March 1992 and May 1992 “A Compaction Study of Directly Compressible Vitamin Preparations for the Development of a Chewable Tablet, Parts I and II. Konkel, P. and Mielck, B.

64 19 August Pharm Tech, March 1994 “Computer Assisted Experimental Design in Pharmaceutical Formulation.” Dobberstein, R. et all.

65 19 August Pharm Tech, April 1998 “A Unique Application of Extrusion for the Preparation of Water Soluble Tablets.” Murphy, M. and Hollenbeck, R.

66 19 August Pharm Tech, June 2000 “Artificial Neural Network and Simplex Optimization for Mixing of Aqueous Coated Beads to Obtain Controlled Release Formulations.” Vaithiyalingam, S. et all.

67 19 August Summary Looked at 13 Case studies Shown 3 types of analysis Shown several areas of application Illustrated how to get started Shown that Q8 QbD has a precedent DOE has been used for a long time

68 19 August Acknowledgements The University of Adelaide Library is the owner of the image of Sir R. A. Fisher. Pharmaceutical Technology holds the copyright for the journal articles used in this presentation. Opinions in this presentation are that of Lynn Torbeck alone.

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